Abstract

This thesis focuses on a novel method for semantic searching and retrieval of
information about learning materials. Metametadata encapsulate metadata
instances by using the properties and attributes provided by ontologies rather
than describing learning objects. A novel metametadata taxonomy has been
developed which provides the basis for a semantic search engine to extract,
match and map queries to retrieve relevant results. The use of ontological views
is a foundation for viewing the pedagogical content of metadata extracted from
learning objects by using the pedagogical attributes from the metametadata
taxonomy. Using the ontological approach and metametadata (based on the
metametadata taxonomy) we present a novel semantic searching mechanism.
These three strands – the taxonomy, the ontological views, and the search
algorithm – are incorporated into a novel architecture (OMESCOD) which has
been implemented, and results of using OMESCOD have been used to evaluate
the effectiveness of the metametadata approach, and the recall and precision of
the search algorithm are compared with search algorithms based on metadata and
on ontologies.
The results support the research hypothesis that using metametadata can
effectively represent the semantic relationships between learning object
metadata.